Taught by: Mark Goldman, UC Davis (November 21, 2016)
Description: This tutorial describes how to apply linear network theory to the analysis and interpretation of neural data. It introduces the concept of “sloppy models” that capture a common problem in model-fitting, in which individual model parameters are poorly constrained by available data (i.e. have “poorly/sloppily constrained parameter values”). Simple methods are illustrated for describing which combination of parameters most affect a particular model fit. This material is relevant to problems in neuroscience involving the interpretation of multidimensional data from recurrently connected systems.
Slides:
- Linear Network Theory and Sloppy Models (PPT) (Mark Goldman’s lecture slides)
- Introduction to Linear Algebra (PPT) (Mark Goldman and Emily Mackevicius slides)
Additional Resources:
